Available online http://breast-cancer-research.com/content/8/4/R34
Research article
Open Access
Vol 8 No 4
Intrinsic molecular signature of breast cancer in a
population-based cohort of 412 patients
Stefano Calza1,4, Per Hall1, Gert Auer2, Judith Bjöhle2, Sigrid Klaar2, Ulrike Kronenwett2,
Edison T Liu3, Lance Miller3, Alexander Ploner1, Johanna Smeds2, Jonas Bergh2 and Yudi Pawitan1
1Department
of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobel väg 12A, SE-171 77 Stockholm, Sweden
of Oncology and Pathology, Cancer Center Karolinska, Radiumhemmet, Karolinska Institutet and University Hospital, Solna SE-171 76
Stockholm, Sweden
3Genome Institute of Singapore, 60 Biopolis Street, #02-01, Genome, 138672 Singapore
4Section of Medical Statistics and Biometry, Department of Biotechnologies and Biomedical Sciences, Viale Europa 11, 25123 Brescia, University
of Brescia, Italy
2Department
Corresponding authors: Jonas Bergh, [email protected], Yudi Pawitan, [email protected]
Received: 22 Feb 2006 Revisions requested: 5 Apr 2006 Revisions received: 2 May 2006 Accepted: 21 Jun 2006 Published: 17 Jul 2006
Breast Cancer Research 2006, 8:R34 (doi:10.1186/bcr1517)
This article is online at: http://breast-cancer-research.com/content/8/4/R34
© 2006 Calza et al.; licensee BioMed Central Ltd.
This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Background Molecular markers and the rich biological
information they contain have great potential for cancer
diagnosis, prognostication and therapy prediction. So far,
however, they have not superseded routine histopathology and
staging criteria, partly because the few studies performed on
molecular subtyping have had little validation and limited clinical
characterization.
Methods We obtained gene expression and clinical data for
412 breast cancers obtained from population-based cohorts of
patients from Stockholm and Uppsala, Sweden. Using the
intrinsic set of approximately 500 genes derived in the Norway/
Stanford breast cancer data, we validated the existence of five
molecular subtypes – basal-like, ERBB2, luminal A/B and
normal-like – and characterized these subtypes extensively with
the use of conventional clinical variables.
Results We found an overall 77.5% concordance between the
centroid prediction of the Swedish cohort by using the Norway/
Stanford signature and the k-means clustering performed
internally within the Swedish cohort. The highest rate of
discordant assignments occurred between the luminal A and
luminal B subtypes and between the luminal B and ERBB2
Introduction
A significant reduction of disease burden from breast cancer
can be achieved by a better characterization of its potentially
fatal forms. Spread of the disease, cell structure, differentiation
and growth patterns have been used to classify tumors and to
subtypes. The subtypes varied significantly in terms of grade (p
< 0.001), p53 mutation (p < 0.001) and genomic instability (p =
0.01), but surprisingly there was little difference in lymph-node
metastasis (p = 0.31). Furthermore, current users of hormonereplacement therapy were strikingly over-represented in the
normal-like subgroup (p < 0.001). Separate analyses of the
patients who received endocrine therapy and those who did not
receive any adjuvant therapy supported the previous hypothesis
that the basal-like subtype responded to adjuvant treatment,
whereas the ERBB2 and luminal B subtypes were poor
responders.
Conclusion We found that the intrinsic molecular subtypes of
breast cancer are broadly present in a diverse collection of
patients from a population-based cohort in Sweden. The
intrinsic gene set, originally selected to reveal stable tumor
characteristics, was shown to have a strong correlation with
progression-related properties such as grade, p53 mutation and
genomic instability.
make a clinically relevant assessment of their metastatic
potential. With an increasing use of screening programs, more
early-stage tumors are diagnosed, creating a need for new
tools for prognostication and therapy prediction. Standard
clinical factors often lead to undertreatment and overtreatment
CMF = cyclophosphamide–methotrexate–5-fluorouracil; ER = estrogen receptor; HRT = hormone replacement therapy; SSI = stemline-scatter index.
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Calza et al.
of large patient cohorts. The classical factors also offer little or
no insight into the pathogenesis and biological separation of
distinct subtypes of human breast cancers.
The emergence of powerful molecular techniques allowing
genome-wide analysis has the potential to improve tumor classification markedly. Using microarray-based gene expression
analyses, Sorlie and colleagues [1] described five subtypes of
breast cancer, roughly dividing estrogen receptor (ER)-negative and ER-positive tumors into two and three subclasses,
respectively. Molecular techniques are growing more and
more important as diagnostic and prognostic tools, but they
have not yet come into routine clinical practice. One reason
could be there had been limited validation [2] as well as limited
clinical description [1] of the molecular subtypes. Validation is
often difficult in this context, because results are influenced
not only by patient selection but also by the choice of methods
used to analyze gene expression data.
Consequently, our aims for this study were, first, to validate the
previously derived molecular subtypes on a large populationbased cohort of 412 patients in Sweden, and second, to characterize the genetic subtypes with the use of routine clinical
variables, and to achieve these goals by using transparent and
widely accepted analysis methods.
Materials and methods
The total study population consisted of 412 patients for whom
we had quality-controlled RNA expression microarray data,
including 159 patients from Stockholm and 253 from Uppsala.
The Stockholm subcohort includes all breast cancer patients
that were operated at the Karolinska Hospital from 1 January
1994 to 31 December 1996 identified from the populationbased Stockholm–Gotland breast cancer registry established
in 1976. The ethical committee at the Karolinska University
Hospital approved this microarray expression project. This
cohort has previously been described in detail [3,4].
Study population
The Stockholm–Gotland Breast Cancer Registry, supplemented with patient records, were examined for information on
tumor size, number of retrieved and metastatic axillary lymph
nodes, hormonal receptor status, distant metastases, site and
date of relapse, initial therapy, therapy for possible recurrences, and date and cause of death. Tamoxifen and/or goserelin were normally used for hormonal treatment, whereas
mostly six courses of intravenous cyclophosphamide–methotrexate–5-fluorouracil (CMF) on days 1 and 8 were used as
adjuvant chemotherapy, except for high-risk patients. After primary therapy, patients were recommended to have regular
clinical examinations and yearly mammograms, in addition to
laboratory and X-ray tests guided by clinical signs and symptoms. Patients followed-up outside the Karolinska Hospital
were tracked by using their unique Swedish personal identification number. There was no loss to follow-up.
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Relapse site, date of relapse, relapse therapy, and date of
death were ascertained until May 2002. The average follow-up
was 6.1 years. Cause of death was coded as death due to
breast cancer (including those with distant metastases, but
dying from other causes), death due to other malignancies,
and death due to non-malignant disorders. Subsequent primary malignancies were identified through the populationbased Swedish Cancer Registry.
A second data source (referred to as Uppsala subcohort) consists of a population-based cohort of primary breast cancer
patients receiving primary therapy from 1987 to 1989 in the
county of Uppsala, Sweden. From the initial set of 315
patients, representing 65% of all breast cancer patients in the
Uppsala county during these years, we were able to obtain
quality-controlled RNA expression profiles from 253 frozen
tumors. The patients were followed until December 1999.
Overall, 76 lymph-node-positive and 26 lymph-node-negative
patients received adjuvant therapy, mostly intravenous CMFbased therapy or adjuvant tamoxifen, whereas 130 patients
did not receive adjuvant therapy. The ethical committee at the
Karolinska Institutet approved this RNA expression study, and
consent was obtained from each patient for the use of the biological tissues.
RNA preparation
RNA was prepared with an RNeasy spin column kit (Qiagen,
Valencia, CA, USA). Frozen tumors were cut into minute
pieces and homogenized for 40 seconds in RNeasy lysis
buffer. Proteinase K was added [5] and the samples were
incubated for 10 minutes at 55°C, followed by centrifugation
and the addition of ethanol. After the transfer into RNeasy columns, DNase was added to increase RNA quality. RNA quality
was assessed with an Agilent 2100 bioanalyzer (Agilent Technologies, Rockville, MD, USA). The material was stored at 70°C. The amount of RNA for each probe preparation varied
between 2 and 5 µg. First-strand cDNA synthesis was generated by using a T7-linked oligo(dT) primer, followed by second-strand synthesis. The in vitro transcription reactions were
performed in batches to generate biotinylated cRNA targets,
which were subsequently chemically fragmented at 95°C for
35 minutes.
Fragmented, biotinylated cRNA (10 µg) was hybridized at
45°C for 16 hours to Affymetrix high-density oligonucleotide
HG-U133AB gene-chip arrays. The arrays were then washed
and stained with streptavidin–phycoerythrin (10 µg/ml). Signal
amplification was achieved with a biotinylated anti-streptavidin
antibody. The scanned images were inspected for the presence of artifacts. In case of defects, the hybridization procedure was repeated. Expression values and detection calls
were computed from raw data by following the procedures
outlined for the Affymetrix MAS 5.0 analysis software [6]. Global mean normalization of MAS 5.0 expression was performed
Available online http://breast-cancer-research.com/content/8/4/R34
on a logarithmic scale to reduce differences in chip intensity
[7].
A sample was either relabeled and the hybridization repeated,
or excluded from further analysis if a scaling factor greater than
four was necessary, if less than 30% present calls were found,
or if the squared multiple correlation coefficient of the expression data on the array to all other arrays was below 0.6.
Microarray data analysis and clustering methods
A publicly available set of 122 tumor samples [2] was downloaded from the Stanford Microarray Database [8]. Mean-normalized log2 ratios of expression values were used in the
analysis. The so-called 'intrinsic' gene list as described by Sorlie and colleagues [2] was used to select genes of interest.
CloneID and Unigene ID were matched by using the Stanford
SOURCE Search website [9]. A total of 516 genes out of the
original 552 had valid Unigene IDs. We excluded 32 genes
assigned to multiple Unigene clusters and based our analyses
on the resulting 484 genes with unique Unigene IDs.
Gene expression data from HG-U133AB Affymetrix chips
were available for the Swedish samples. Matching between
the Norway/Stanford data and the two Swedish data sets was
achieved by using Unigene IDs build 181. In all, there were
465 genes with unique Unigene IDs that matched the Stanford intrinsic gene set.
Genes were median-centered and subtypes were defined by
a hierarchical clustering of the Norway/Stanford data, using
uncentered correlation as a distance metric and an averagelinkage clustering algorithm. This step attempts only to reproduce the results of Sorlie and colleagues [2], so we expect to
find their five subtypes (basal-like, ERBB2, luminal A, luminal
B and normal-like). The profile of a subtype – the so-called
'centroid' – was computed by averaging expression values
across each gene within each of the subtypes. Thus, the centroid simply represents the average gene expression over the
tumors in a subtype. To obtain better defined and more homogeneous genetic profiles, we used only representative tumor
samples in defining the centroids; representative was defined
as having Pearson correlation of at least 0.2 with all other samples in the same subtype class.
Consistency of clustering results
We then assigned the Swedish tumor samples to the five subtypes discovered in the Norway/Stanford data. This was
achieved by calculating the Pearson correlation between each
sample and each of the five centroids. Samples were then
assigned to the subtype of the centroid with the largest correlation coefficient; we will refer to this procedure as 'centroid
prediction'. If the correlations with all five centroids were
below 0.1, a sample was labeled as 'unclassified'.
The centroid prediction of tumor samples to specific subtypes
as described above represents a classification procedure,
using Norway/Stanford data as the training set; that is, we
forced the samples to fit into one of five prespecified categories. We evaluated whether this class allocation was consistent with the k-means clustering of the samples themselves. If
there had been little consistency between the centroid prediction and the k-means clustering, then the class assignment
would have been spurious. In this analysis we excluded the
unclassified samples.
In brief, samples were scaled to have a mean of 0 and a standard deviation of 1, and clustered with a k-means algorithm with
the number of cluster centers set to k = 5. The resulting five
clusters were matched to the centroid-predicted labeling in
such a manner as to maximize their agreement. The scaling
step was included to make the Euclidean distances used in
the k-means algorithm more similar to the correlation distances used in the centroid prediction. To allow for potential
systematic differences in sample handling, the Stockholm and
Uppsala cohorts were clustered separately. Concordance is
expressed as the overall percentage of samples classified in
the same group by the centroid prediction and k-means clustering methods. Intuitively, if this procedure is applied to randomly generated data, there will be little concordance
between the two methods. Any discordant assignment indicates some similarity or overlapping characteristics between
two subtypes.
Clinical and survival data analysis
The genetically derived subtypes were characterized on a
number of clinical characteristics, including age at diagnosis,
tumor size, lymph-node status, grade and receptor status, and
use of hormone replacement therapy (HRT). Mutation of p53
was ascertained by complete sequencing of the p53 gene
[10]. Genomic instability was assessed on the basis of image
cytometric data of DNA content; tumors were classified into
diploid, aneuploid or tetraploid groups, and also whether they
were genomically stable or unstable according the stemlinescatter index (SSI) developed by Kronenwett and colleagues
[11]. SSI is the sum of the percentages of cells with DNA content values in the S-phase region (S phase), plus the percentage of cells with DNA content values exceeding twice the
modal value plus 1c (G2 exceeding rate, or G2 Exc), plus the
coefficient of variation of the tumor stemline. A genomically
unstable tumor is defined to have an SSI of more than 8.8, indicating a significant cell-to-cell variation in DNA content.
Finally we performed a survival analysis, considering as outcome both the relapse-free interval and the occurrence of
death due to either breast cancer or distant metastases. Given
their uncertain biological interpretation, 43 unclassified samples (n = 20 in Stockholm and n = 23 in Uppsala) were
excluded from the analysis. To obtain homogeneously defined
treatment groups, we also excluded from the Stockholm
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Calza et al.
reported by Sorlie and colleagues [2] were identifiable, and
93% of the tumors clustered in the same way. Moreover, the
clusters were characterized by the same main genes as previously described, including ERBB2, GRB7 and PPARBP for
the ERBB2 cluster, CXCL1, KRT5, KRT17 and TRIM29 for
the basal-like cluster, ESR1, GATA3 and SCUBE2 XBP1 for
luminal A, SQLE, GGH and LAPTM48B for luminal B, and
PIK3R1 and AKR1C1 for normal-like samples (Figure 2S in
Additional file 1).
Table 1
Norway/Stanford centroid size (N), minimum subtype
correlation (R) and centroid-prediction assignments
Subtype
Norway/Stanford
Sweden
N
N
R
Percentage
Basal-like
17
0.37
59
14.3
ERBB2
7
0.23
43
10.4
Luminal A
9
0.27
122
29.6
Luminal B
7
0.20
54
13.1
Normal-like
7
0.35
91
22.1
Unclassifie
d
-
-
43
10.4
Subtype centroids were then defined by including tumors with
a pairwise correlation higher than 0.2, as shown in Table 1. All
the samples considered in the centroids were also part of the
centroids defined by Sorlie and colleagues [2], except for the
ERBB2 centroid, where three samples out of seven were
unclassified by Sorlie and colleagues [2]. The centroids are
marked in a dendrogram in Figure 1S in Additional file 1.
Samples with a Pearson correlation of less than 0.1 were labeled as
unclassified.
Swedish data
Using centroid prediction towards the five subtypes in the Norway/Stanford data, 59 samples were classified as basal-like,
43 as ERBB2, 122 as luminal A, 54 as luminal B and 91 as
normal-like tumors (Table 1). A total of 43 samples showed a
correlation of less than 0.1 with any centroid and were consequently put into the unclassified group.
cohort any patients with no adjuvant therapy and those treated
with chemotherapy. In Stockholm, post-operative adjuvant
therapy was a standard procedure; patients who did not
receive the therapy either denied it or were considered too
unfit for treatment. In contrast, the adjuvant-untreated patients
in Uppsala were part of a clinical trial. We analyzed separately
the endocrine-treated patients (n = 171) and those who did
not receive any adjuvant therapy (n = 130). Among the endocrine-treated patients there were 16 patients who also
received chemotherapy. To maintain power, these were kept in
the analysis. Univariate Cox proportional models considered
as a unique predictor variable the tumor subtype. All of the statistical analysis was performed with R [12] and Bioconductor
[13], whereas clusters were displayed with treeview [14].
The data were then clustered by using the same hierarchical
clustering procedure as for the Norway/Stanford data; the
dendrograms are displayed in Figures 3S and 4S in Additional
file 1. As reported previously [15,16], the main separation was
between tumors overexpressing estrogen-related genes, previously labeled as normal-like and luminal A/B, versus tumors
negative for these genes, including the basal-like and ERBB2
subtypes. However, in contrast with the Norway/Stanford and
Stockholm data, almost all Uppsala samples classified as luminal B (28 of 31) clustered in the same main branch as basallike and ERBB2 clusters, whereas normal-like tumors were
located close to the luminal A group.
Results
Norway/Stanford data
We first analyzed the Norway/Stanford data to establish the
previous molecular subtypes: basal-like, ERBB2, luminal A,
luminal B and normal-like. This was achieved by hierarchical
clustering of the 122 samples from Norway/Stanford based on
516 genes (Figure 1S in Additional file 1). The five subgroups
Table 2
Concordance between centroid-prediction classification (rows) and k-means clustering label assignment (columns) for the Swedish
cohort
Subtype
Basal-like
ERBB2
Luminal A
Luminal B
Normal-like
Total
Basal-like
46
12
0
0
1
59
ERBB2
0
39
0
1
3
43
Luminal A
0
1
87
34
0
122
Luminal B
0
21
0
32
1
54
Normal-like
2
0
5
2
82
91
Total
48
73
92
69
87
369
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Available online http://breast-cancer-research.com/content/8/4/R34
Most samples associated with the basal-like subtype clustered together, showing a distinctive genetic profile, characterized by the overexpression of KRT genes and TRIM29,
among others. Other subtypes were also characterized by the
corresponding genes found in the Norway/Stanford data (Figure 5S and 6S in Additional file 1). Consistent with previous
findings is the observation that the basal-like cluster is the
most homogeneous in both Swedish cohorts, with a high correlation among samples and a clear separation from other subtypes.
Consistency of clustering result
In the Swedish cohort, the overall concordance rate between
the centroid prediction and the k-means clustering was 77.5%
(Table 2), comparable to the 74.6% concordance rate
observed for the Norway/Stanford data (data not shown). The
highest rate of discordant assignments occurred between the
luminal A and luminal B subtypes, and between the luminal B
and ERBB2 subtypes. Thirty-four (9%) tumors were classified
as luminal A by the centroid prediction but clustered as luminal
B in the k-means clustering. A total of 21 (6%) tumors were
classified as luminal B by the centroid prediction but clustered
as ERBB2 in the k-means clustering. Twelve tumors had discordant assignments to the basal and ERBB2 subtypes. The
normal-like subtype had the fewest discordant assignments.
To understand the reasons for discordant assignments, we
investigated the expression of the distinguishing genes of
each subtypes. The 21 luminal B tumors clustered to ERBB2
had relatively high expression of the ERBB2 and GRB7 genes
and low expression of ESR1 gene, all characterizing the
ERBB2 subtype. The 34 luminal A tumors clustered to luminal
B displayed a high expression of GGH and SQLE, but low
expression of SCUBE2, NAT1 and LTF [17]. In comparison
with those consistently classified as basal-like (n = 46), the
tumors clustered as ERBB2 (n = 12) showed a lower average
expression in KRT17, KRT5 and especially GABRP, which
has been reported to be associated with ER-/HER2- breast
cancers [18].
Tumor subtypes and estrogen receptor status
Breast cancer subtype classification is closely related to ER
status, with a high proportion of luminal A/B and normal-like
tumors having ER-positive protein overexpression, whereas
basal-like and ERBB2 tumors have a higher proportion of ER
negativity [15,16,19]. These findings were confirmed in the
Swedish data (Table 3). However, a large discrepancy was
seen in the proportion of ER-positive tumors between the
basal-like (45.8%) and ERBB2 (67.4%) groups. This discrepancy is surprising because we did not see any obvious difference in the expression of the ESR1 gene (Figure 1). For the
luminal A/B and normal-like subtypes, the expression of the
ESR1 gene seems consistent with the protein expression.
Tumor subtypes and clinical characteristics
When the molecular subtypes were characterized according
to tumor characteristics, treatment and HRT use, quite a complex pattern emerged (Table 4). The youngest women were
found in the basal-like group, in which 27.5% were premenopausal. The average tumor size and the proportion of tumors
smaller than 21 mm were not dramatically different among the
groups, with the exception of the ERBB2 group, in which the
tumor size tended to be larger. Basal-like tumors tended to
have high Elston grade and to be genomically unstable, but
surprisingly there was no significant difference in lymph-node
metastasis status between the different subtypes. A majority
(65%) of basal-like tumors had p53-sequence mutations and
a relatively high 40% of the patients were current or former
users of HRT.
The ERBB2 group consisted of elderly women with large
tumors, of which 57% were Elston grade III, 39% had a p53
mutation and 71% were genomically unstable aneuploids, an
even higher percentage than in the basal-like subtype. At the
same time these patients were 67% ER-positive and 72% progesterone receptor-positive. The luminal B group revealed the
same complex pattern as ERBB2 but on a less aggressive
scale, particularly with a smaller proportion of unstable aneuploid tumors. Tumor size and receptor status indicated a low
metastatic potential, yet 55% had an Elston grade of III.
Found mostly in postmenopausal women, the luminal A and
normal-like tumors tended to be small and receptor positive,
were unlikely to be Elston grade III, tended to have wild-type
p53 status and tended to be genomically more stable. One
striking difference between these two subtypes was in the
ongoing use of HRT: 51% in the normal-like group versus 5%
in luminal A.
Table 3
Estrogen receptor (ER) status by tumor subtype in the Swedish cohort
ER status
Basal-like
ERBB2
Luminal A
Luminal B
Normal-like
ER-negative
32
14
3
7
7
ER-positive
27
29
115
47
84
45.8
67.4
97.5
87.0
92.3
ER-positive (%)
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Calza et al.
Figure 1
Figure 2
Logarithmic expression of the ESR1 gene.
gene Boxplots are shown of
ESR1 gene expression values (log2 transformed) against subtype in
both Swedish cohorts.
Tumor subtypes and prognosis
Genetically derived subtypes seem to be distinct biological
entities, so we expect them to have prognostic implications.
Overexpression of the ERBB2 gene has been associated with
poor clinical outcome, whereas there is some indication of
poor prognosis for tumors overexpressing citokeratin 17 and
5, characteristics of the basal-like subtype [16,20,21]. Previous findings [15] suggest that prognostic discrepancies
between subtypes reflect different responses to therapy,
especially endocrine therapies. To gain a better insight we performed separate analyses for patients with endocrine therapy
and for those without any adjuvant treatment. However, interpretation is still rather limited as a result of the small number of
patients.
Regardless of whether or not the patient received adjuvant
therapy, the ERBB2 group had the worst recurrence-free survival, whereas the luminal A and normal-like groups had the
best prognosis (Figure 2). Among endocrine-treated patients,
the luminal B subtypes had a significantly lower survival than
the luminal A and normal-like tumor groups, and the patients in
the basal-like group did not differ significantly from those in the
luminal A group. Among the untreated patients, however, the
basal-like group had a survival pattern more similar to the luminal-B and ERBB2 patients. Similar results were obtained for
breast cancer-specific survival (Figure 7S in Additional file 1).
Discussion
We were able to confirm that the intrinsic signature described
previously [1] exists in a broad population-based cohort of
breast cancers. This is by far the largest validation and clinical
characterization of the molecular subtypes to date. Previous
characterization was given in [1] – p53 mutation status on 69
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Kaplan-Meier relapse-free survival curves.
curves Survival curves are shown for
the tumor subtypes in (a) patients with endocrine therapy and (b)
patients without any adjuvant therapy. The p value, computed with Cox
regression, is for simultaneous comparison of all the curves.
cases – and in an unpublished report [22], reviewed in [23].
The different prognosis of the subtypes was already reflected
in the tumor characteristics, although the patterns were complex. By performing a separate analysis of endocrine-treated
and adjuvant-untreated patients we were also able to evaluate
the differences between subtypes in terms of both prognosis
and response to therapy.
Normal-like tumors were small, found in postmenopausal
women, seldom of Elston grade III, mostly stable diploid and
more prevalent among HRT users. They had a favorable prognosis under endocrine therapy. Luminal-A tumors closely
resembled normal-like tumors but were not associated with
HRT use and had a superior prognosis regardless of adjuvant
therapy. In contrast, the ERBB2 tumors showed a 50%
relapse-free 5-year survival not influenced by therapy. The
poor prognosis was reflected in large tumor size, high propor-
Available online http://breast-cancer-research.com/content/8/4/R34
Table 4
Descriptive statistics by tumor subtypes in the Swedish cohort
Characteristic
Basal-like
ERBB2
Luminal A
Luminal B
Normal-like
n = 59
n = 43
n = 122
n = 54
n = 91
p
0.51e
Cohort (n)
Stockholm
25
15
39
23
37
Uppsala
34
28
83
31
54
54
59
66
61
59
Mean age at breast cancer diagnosis
<0.001e
0.0807e
Menopausal status (%)
Premenopausal
27.5
13.3
10.9
24.4
18.6
Postmenopausal
72.5
86.7
89.1
75.6
78.6
Mean tumor size (mm)
22.9
27.9
21.8
22.6
20.7
0.0345f
51
35
57
42
70
<0.001e
Tumor size < 21 mm (%)
<0.001e
Elston grade
I
3.5
4.8
34.5
5.7
43.2
II
21.1
38.1
54.3
39.6
52.3
75.4
57.1
11.2
54.7
4.5
64.7
39.3
9.6
38.7
3.7
III
p53 mutation
(%)a
HRT (%)b
<0.001e
<0.001e
Never
60.0
78.6
81.6
82.6
48.6
Former
8.0
7.1
13.2
8.7
0.0
Ongoing
32.0
14.3
5.3
8.7
51.4
29
48
33
41
33
0.312e
Estrogen receptor positive (%)
45.8
67.4
97.5
87.0
92.3
<0.001e
Progesterone receptor positive (%)
54.2
72.1
93.2
77.8
92.3
<0.001e
76.0
78.6
56.4
78.3
37.1
0.0031e
Lymph-positive (%)
Genomically unstable
(%)b
Genomic instability by ploidyb
Diploid stable
0.0123e
12.0
7.1
25.6
4.3
40.0
Diploid unstable
4.0
0.0
20.5
26.1
17.1
Tetraploid stable
4.0
7.1
7.7
8.7
8.6
Tetraploid unstable
16.0
7.1
12.8
13.0
0.0
Aneuploid stable
8.0
7.1
10.3
8.7
14.3
Aneuploid unstable
56.0
71.4
23.1
39.1
20.0
Treatment type
(%)c
0.0353e
None
39.7
35.7
43.3
35.8
46.2
Endocrine
36.2
45.2
50.8
56.6
44.0
Chemotherapy
15.5
16.7
5.0
5.7
7.7
Radiotherapy
8.6
2.4
0.8
1.9
2.2
30
51
17
43
19
Breast cancer event within 5 years (%)d
<0.001e
ap53
data were available only for the Uppsala cohort. bHRT and genomic instability were available only for the Stockholm cohort. cFor the Stockholm
cohort, endocrine comprises endocrine treatment alone and with any other combination. Chemotherapy refers to chemotherapy alone or associated
with radiotherapy. Radiotherapy means radiotherapy alone. dBreast cancer event refers to relapse or death for breast cancer. ePearson χ2 test.
fAnalysis of variance test.
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Breast Cancer Research
Vol 8 No 4
Calza et al.
tion of Elston grade III and unstable aneuploids, and few ERpositive tumors. Luminal B tumors had a clearly worse prognosis than luminal A ones, and were apparently less responsive
to the hormonal therapy than the basal-like and normal-like
tumors [2]. In comparison with the luminal A group, this group
had a higher proportion of Elston grade III, p53 mutation and
lymph-node positive tumours, confirming the hypothesis of a
more proliferative profile [23]. Surprisingly, although the basallike tumors had even fewer ER-positive tumors and more highgrade tumors, they had better relapse-free survival than the
ERBB2 tumors, especially among the endocrine-treated
patients. It is worth noting that about one-third of the patients
with basal-like tumors used HRT at the time of diagnosis.
The intrinsic signature set of approximately 500 genes was
originally selected on the basis of their stable expression
between pairs of samples taken from the same tumors, before
therapy and after 15 weeks of neoadjuvant therapy [1]. This
implies that the set is enriched in genes whose expression patterns are characteristic for individual tumors as opposed to
those that vary as a function of tissue sampling, and hence
would be ideally suited for classification. Another recent study
[24] showed that the subtypes were also observed among
Asian-Chinese patients, although this study performed only
the hierarchical clustering and identified only three subtypes.
The discordant subtyping between the centroid prediction and
k-means clustering indicates some overlapping characteristics
between subtypes, and potentially reveals a biological heterogeneity more complex than the subtypes alone. Discordances
occurred most frequently between the luminal A and luminal B
groups and between the luminal B and ERBB2 groups. Not
surprisingly, despite differences in receptor status, luminal B
and ERBB2 share similar clinical – such as tumor grade – and
prognostic characteristics. This suggests that there must in
fact be grade-associated and outcome-associated genes in
the intrinsic gene set, and that these two subtypes have similar
expression in these genes. A similar argument can also be
made from the large confusion of luminal A and luminal B,
namely that this must have been driven by ER-associated
genes, because these two subtypes have similar receptor
characteristics. Having the fewest discordances, the normallike subtype seemed to have the most distinctive profile. One
of the most striking characteristics was the high proportion of
HRT users within this subtype.
Although the intrinsic signature is expected to reveal fundamental tissue properties, such as origin (basal-like versus luminal), our study supports the hypothesis that the subtypes are
distinct biological entities with distinct clinical characteristics.
Our study showed how they differed in progression-related
characteristics, such as tumor grade, p53 mutation and
genomic instability. This means that there must be a strong
correlation between the intrinsic gene set and other progression-related signatures based on p53 transcription profile [4],
Page 8 of 9
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proliferation or genomic instability, and possibly an inherent
capability to metastasize [25]. The extent to which the different
signatures contribute independent information toward clinical
progression or prognosis requires further study.
Our findings further support the hypothesis of a reduced
response to hormone-based treatments for ERBB2 and luminal B tumors [23]. Whereas luminal A tumors seem to have a
good prognosis overall, luminal B tumors had a poor prognosis
regardless of endocrine therapy, indicating some resistance to
therapy. This may be explained at least partly by a worse clinical profile, with a higher tumor grading and a higher proportion
of p53 mutation. In contrast, in the endocrine-treated group
the basal-like subtype seemed to survive better than expected
considering its poor clinical characteristics including its high
proportion of Elston grade III (75%) and few ER-positive
(46%) tumors. The ERBB2 tumors had the lowest relapse-free
survival: approximately 50% of the patients had either died
from breast cancer or experienced distant metastases. Overexpression of the human transmembrane tyrosine kinase
growth factor receptor (HER-2), characteristic of the ERBB2
group, has been associated with more aggressive forms of
tumor and with resistance to endocrine therapies [26-28].
Conclusion
One strength of our study was that we were able to analyze
hormone-adjuvant-treated and untreated patients separately.
Thus, it seems that the molecular subtypes also carry therapy
predictive information. However, when interpreting the results
it should be emphasized that observational studies are not
optimal for evaluating therapy. Reverse causality, in which a
patient with a more aggressive tumor is more likely to receive
therapy, is a major problem, producing an apparent result that
therapy worsens survival. Ideally, to establish microarraybased predictive markers we would use a randomized comparison of adjuvant therapy versus placebo, but we are not currently aware of any such study.
Finally, from the clinical perspective, it is of highest importance
to subgroup and characterize cancers so as to identify
patients in need of aggressive therapy and those that could
probably be spared adjuvant systemic therapy and thereby the
adverse late health effects. We have been able to verify that
the intrinsic signature classifies tumors into five groups with
distinct tumor characteristics influencing relapse-free survival
and probably also predictive of therapy response.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
SC, YP and AP performed the biostatistical analyses and cowrote the manuscript. PH and J Bergh co-wrote the manuscript. J Bjöhle contributed tumors, coordinated tissue and
clinical data collection and the extraction of RNA for the Stock-
Available online http://breast-cancer-research.com/content/8/4/R34
holm cohort. UK and GA collected data on genomic instability.
SK, ETL and LM contributed tumors, coordinated the collection of tissue, and also clinical and microarray data for the Uppsala cohort. All authors read and approved the final
manuscript.
Additional files
The following Additional files are available online:
Additional file 1
A PDF file containing seven supplementary figures
(Figures 1S – 7S).
See http://www.biomedcentral.com/content/
supplementary/bcr1517-S1.pdf
Acknowledgements
We are grateful to Lambert Skoog, Marianne Frostvik and Torsten
Hägerström for excellent assistance in managing the tumor bank, handling the frozen tumors and for excellent RNA preparations and quality
controls. The study was supported by grants from the Swedish Cancer
Society, the King Gustav V Jubilee Fund and the Cancer Society in
Stockholm, Sweden, and received funding from the Agency for Science,
Technology, and Research (Singapore).
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